FIN7030: Times Series Financial Econometrics 2

# FIN7030: Times Series Financial Econometrics 2
## Meet the toolkit:<br>programming
### Barry Quinn
### 2022-01-31

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<span>
<a href="https://quinference.com" target="_blank"><b>quinference.com</b></a> - Dr. Barry Quinn
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## Course toolkit

<br>

.pull-left[
### .gray[Course operation]
.gray[
- Slack
- Canvas
- Teams
- Github
]
]
.pull-right[
### .pink[Doing financial data science]
- .pink[Programming:]
  - .pink[R]
  - .pink[RStudio]
  - .pink[tidyverse]
  - .pink[R Markdown]
- .gray[Version control and collaboration:]
  - .gray[Git]
  - .gray[GitHub]
]

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## Learning goals

By the end of the course, you will be able to...

- gain insight from data

--
- gain insight from data, **reproducibly**

--
- gain insight from data, reproducibly, **using modern programming tools and techniques**

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- gain insight from data, reproducibly **and collaboratively**, using modern programming tools and techniques

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- gain insight from data, reproducibly **(with literate programming and version control)** and collaboratively, using modern programming tools and techniques

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# Reproducible data analysis

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## Reproducibility checklist

Near-term goals:

- Are the tables and figures reproducible from the code and data?
- Does the code actually do what you think it does?
- In addition to what was done, is it clear *why* it was done?

Long-term goals:

- Can the code be used for other data?
- Can you extend the code to do other things?

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## Toolkit for reproducibility

- Scriptability `$\rightarrow$` R
- Literate programming (code, narrative, output in one place) `$\rightarrow$` R Markdown
- Version control `$\rightarrow$` Git / GitHub

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## R and RStudio

.pull-left[
<img src="data:image/png;base64,#img/r-logo.png" width="25%" />
- R is an open-source statistical **programming language**
- R is also an environment for statistical computing and graphics
- It's easily extensible with *packages*
]
.pull-right[
<img src="data:image/png;base64,#img/rstudio-logo.png" width="50%" />
- RStudio is a convenient interface for R called an **IDE** (integrated development environment), e.g. *"I write R code in the RStudio IDE"*
- RStudio is not a requirement for programming with R, but it's very commonly used by R programmers and data scientists
]

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## R packages

- **Packages** are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data<sup>1</sup>

- As of September 2020, there are over 16,000 R packages available on **CRAN** (the Comprehensive R Archive Network)<sup>2</sup>

- We're going to work with a small (but important) subset of these!

<sup>2</sup> [CRAN contributed packages](https://cran.r-project.org/web/packages/).
]

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## Tour: R and RStudio

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## A short list (for now) of R essentials

- Functions are (most often) verbs, followed by what they will be applied to in parentheses:

```r
do_this(to_this)
do_that(to_this, to_that, with_those)
```

- Packages are installed with the `install.packages` function and loaded with the `library` function, once per session:

```r
install.packages("package_name")
library(package_name)
```

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## R essentials (continued)

- Columns (variables) in data frames are accessed with `$`:

```r
dataframe$var_name
```
]

- Object documentation can be accessed with `?`

```r
?mean
```

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## tidyverse

- The **tidyverse** is an opinionated collection of R packages designed for data science
- All packages share an underlying philosophy and a common grammar
]

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## rmarkdown

- **rmarkdown** and the various packages that support it enable R users to write their code and prose in reproducible computational documents
- We will generally refer to R Markdown documents (with `.Rmd` extension), e.g. *"Do this in your R Markdown document"* and rarely discuss loading the rmarkdown package
]

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# R Markdown

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## R Markdown

- Fully reproducible reports -- each time you knit the analysis is ran from the beginning
- Simple markdown syntax for text
- Code goes in chunks, defined by three backticks, narrative goes outside of chunks

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## Tour: R Markdown

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## Environments

Remember this, and expect it to bite you a few times as you're learning to work 
with R Markdown!

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## Environments

```r
x <- 2
x * 3
```
]

```r
x * 3
```
]

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## R Markdown help

.pull-left[
.center[
.midi[R Markdown Cheat Sheet  
`Help -> Cheatsheets`]
]
<img src="data:image/png;base64,#img/rmd-cheatsheet.png" width="80%" />
]
.pull-right[
.center[
.midi[Markdown Quick Reference  
`Help -> Markdown Quick Reference`]
]
<img src="data:image/png;base64,#img/md-cheatsheet.png" width="80%" />
]

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## How will we use R Markdown?

- Every assignment / report / project / etc. is an R Markdown document
- You'll always have a template R Markdown document to start with
- The amount of scaffolding in the template will decrease over the semester

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## What's with all the hexes?

.footnote[
Mitchell O'Hara-Wild, [useR! 2018 feature wall](https://www.mitchelloharawild.com/blog/user-2018-feature-wall/)
]

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## Git and GitHub

.pull-left[
<img src="data:image/png;base64,#img/git-logo.png" width="25%" />
- Git is a version control system -- like “Track Changes” features from Microsoft Word, on steroids
- It's not the only version control system, but it's a very popular one
]
.pull-right[
<img src="data:image/png;base64,#img/github-logo.png" width="25%" />
- GitHub is the home for your Git-based projects on the internet -- like DropBox but much, much better

- We will use GitHub as a platform for web hosting and collaboration (and as our course management system!)
]

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## Versioning

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## Versioning

### with human readable messages

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## Why do we need version control?

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## How will we use Git and GitHub?

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## How will we use Git and GitHub?

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## How will we use Git and GitHub?

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## How will we use Git and GitHub?

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# Git and GitHub tips

- There are millions of git commands -- ok, that's an exaggeration, but there are a lot of them -- and very few people know them all. 99% of the time you will use git to add, commit, push, and pull.

--
- We will be doing Git things and interfacing with GitHub through RStudio, but if you google for help you might come across methods for doing these things in the command line -- skip that and move on to the next resource unless you feel comfortable trying it out.

--
- There is a great resource for working with git and R: [happygitwithr.com](http://happygitwithr.com/). Some of the content in there is beyond the scope of this course, but it's a good place to look for help.

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- Create a GitHub account
- Verify your GitHub email
- Adjust your GitHub settings for a more pleasant GitHub experience
  - Settings > Emails > Uncheck "Keep my email address private"
  - Settings > Emails > Update name and photo
]

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.your-turn[
**Difficulty: medium**
- Go to [Github.com](https://github.com) and create an account using your QUB credentials.
- Login to the [Slack channel](https://join.slack.com/t/rethinkingeco-3ss9716/shared_invite/zt-12nfdfr7o-XAy2Ef1knAKXgUNo2kWw3w) and share your username with the lecturer.
- Create a new repository on Github called 'first repo'
- Create a new project on your Q-RaP RStudio Cloud account by using the url of your first repo
- create a Rmarkdown file in new RStudio project space.
- Knit the Rmarkdown file to a html file.
- Commit and push change to your Github repo.

]